In recent years, mobility empowered by smart phones, tablets and numerous applications running on those mobile devices is transforming the way people live and work in the digital age. Innovations and new business models are emerging that take advantages of this rise of mobile computing. Despite tremendous opportunities promised by the transition to mobility, challenges exist before its full potential can be realized by society as well as by companies. For example, the spread of real-time targeting technologies in mobile display advertising creates a new challenge of how to efficiently allocate countless categories of advertising opportunities, or impressions, in real time. For another example, social broadcasting networks such as Twitter in the U.S. and "Weibo" in China are making it extremely convenient for consumers to spread word-of-mouth (WOM) among them, which both poses new challenges and offers new opportunities to companies wishing to harness the power of consumer WOM. The dissertation contains three essays exploring those issues. In the first essay, the concept of "smart market" for impression allocation is proposed, which emphasizes allocation contingent on uncertain supply and promotes coordination among advertisers across impression categories. A new theory is developed to solve the complicated optimization problem, which leads to a "decomposition and standardization" algorithm. In the second essay, I investigated whether and how Twitter WOM affects movie sales by estimating a dynamic panel data model using publicly available data and well-known machine learning algorithms. I found that chatter on Twitter does matter; however, the magnitude and direction of the effect depends on whom the WOM is from and what the WOM is about. The findings provide new perspectives to understand the effect of WOM on product sales and have important managerial implications. The third essay examines the possibility of designing social-broadcasting-based business intelligence (BI) systems that utilizes real-time information extracted from social broadcasting networks with text mining techniques. A new framework is proposed for this purpose and a Twitter-based BI system is designed and implemented that forecasts movie box office revenues during the opening weekend and daily revenue four weeks after the release of a movie. Preliminary results suggest that social-broadcasting-based BI systems have great potential and are worth exploring by both researchers and practitioners. / text

Mobile phones have become a necessity that is used by people everyday. Enterprise wants to take advantage of this new channel to deliver their advertising messages to the consumer, while the consumers are interested in obtaining new product information. A mobile advertisement platform can bring the enterprise advertiser and the consumer needs together. The main purpose of this research is to explore the current status of mobile advertisement platform and how this kind of platform can be designed. The study includes several major parts: (1) review of literature related to mobile advertising platforms, (2) adopt design science method to analyze system requirements and design a prototype system, (3) use experts to review the system and provide suggestions for improvement. The course of planning and prototype development in a case company are described and evaluated by a group of experts. The contributions of this research are as follows: 1. Understand the current status of mobile advertising to help managers and system developers if they are interested in building such a system. 2. Explore feasible platform implementation through a real prototype development in our case company to show the value and potential of such a platform.

During the dot com era the word 'personalisation' was a hot buzzword. With the fall of the dot com companies the topic has lost momentum. As the killer application for UMTS or the mobile internet has yet to be identified, the concept of Multi-Dimensional-Personalisation (MDP) could be a candidate. Using this approach, a recommendation of mobile advertisement or marketing (i.e., recommendations or notifications), online content, as well as offline events, can be offered to the user based on their known interests and current location. Instead of having to request or pull this information, the new service concept would proactively provide the information and services – with the consequence that the right information or service could therefore be offered at the right place, at the right time. The growing availability of "Location-based Services“ for mobile phones is a new target for the use of personalisation. "Location-based Services“ are information, for example, about restaurants, hotels or shopping malls with offers which are in close range/short distance to the user. The lack of acceptance for such services in the past is based on the fact that early implementations required the user to pull the information from the service provider. A more promising approach is to actively push information to the user. This information must be from interest to the user and has to reach the user at the right time and at the right place. This raises new requirements on personalisation which will go far beyond present requirements. It will reach out from personalisation based only on the interest of the user. Besides the interest, the enhanced personalisation has to cover the location and movement patterns, the usage and the past, present and future schedule of the user. This new personalisation paradigm has to protect the user's privacy so that an approach supporting anonymous recommendations through an extended 'Chinese Wall' will be described.

One goal of every business is to save money, and building strategies that work to minimize spending and maximize profit is key to the success of a company. Cidewalk is a mobile advertisement company that wished to implement an optimal bidding strategy to help reduce the company's cost and in turn maximize their profits. To accomplish this goal we collected market data which was then analyzed to identify the distribution of second best bids, which is the price Cidewalk pays for an advertisement space by bidding in a Vickrey auction. The optimal bidding model was then implemented through a simulation together with Bayesian updating methods to ensure the model would be responsive to changes in the market. The model's performance was evaluated through the simulation and it was discovered that throughout one day the implemented model yielded 17% savings when compared to Cidewalk's current bidding model.

One goal of every business is to save money, and building strategies that work to minimize spending and maximize profit is key to the success of a company. Cidewalk is a mobile advertisement company that wished to implement an optimal bidding strategy to help reduce the company's cost and in turn maximize their profits. To accomplish this goal we collected market data which was then analyzed to identify the distribution of second best bids, which is the price Cidewalk pays for an advertisement space by bidding in a Vickrey auction. The optimal bidding model was then implemented through a simulation together with Bayesian updating methods to ensure the model would be responsive to changes in the market. The model's performance was evaluated through the simulation and it was discovered that throughout one day the implemented model yielded 17% savings when compared to Cidewalk's current bidding model.

在行動裝置當道的社會中，行動廣告已成為業者進行產品推廣的新管道。根據過去文獻指出資訊性、娛樂性、干擾性、可信性皆會影響行動廣告效果，但對於加入適時適地性特質的行動廣告較少多加著墨。因此，本研究以Brackett and Carr (2001)提出的廣告態度模型為基礎，旨在探討行動廣告之定位性與即時性此兩特性對行動廣告效果的影響。本研究採量化分析，透過網路發放問卷，有效樣本共480份，研究中以SPSS與Smart PLS軟體進行結構方程模式分析。 研究結果發現，廣告前因之資訊性與娛樂性同屬一構面，本研究將此構面命名為「資訊娛樂性」。當資訊娛樂性越高時，則可信性越高，廣告效果越佳。但當資訊娛樂性越高時，消費者會覺得廣告帶給他越干擾的感覺。其次，在行動特性的影響之下，定位性與即時性對資訊娛樂性和干擾性有交互作用，表示具定位性與即時性的行動廣告確實會對消費者產生影響力。第三，廣告認知反應中只有可信性對行動廣告效果產生影響，干擾性不會影響行動廣告效果，當可信性越高，則行動廣告效果越正面。最後，資訊娛樂性確實會直接影廣告效果，且資訊娛樂性越高，則消費者對行動廣告的態度、廣告中產品的品牌態度及產品的購買意願都會越高。本研究之結果期望能提供學術界與實務界在行動廣告相關研究，及行動廣告設計與規劃運用之參考。